Machine Learning Prioritization for DevOps Change Requests
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Solution Overview
Problem
In DevOps environments, change requests often lack objective metrics for prioritization, leading to inefficient handling of software changes intended to prevent recurring issues, as developers rely heavily on human judgment rather than quantitative assessments.
Innovation Solution
A method is introduced to link operational data with change requests using machine learning techniques, associating new events with stories and related change requests, calculating a cost that updates the priority of change requests, thereby enhancing the prioritization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If change requests are prioritized using human judgment alone, then subjective expertise can be applied, but objective quantitative metrics are lacking leading to inefficient handling
Solution Approach 1:
The patent replaces the manual human judgment process with an automated machine learning system that uses natural language processing and cost calculation algorithms to objectively prioritize change requests, transforming subjective assessment into quantitative measurement
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between operational data/events and change request prioritization, which processes events, calculates costs, and generates priority scores to bridge the gap between raw data and decision-making
2Extent of automation
If machine learning techniques are used to associate events with stories and change requests, then automated prioritization is achieved, but system complexity increases
Solution Approach 1:
The patent creates a multi-functional machine learning system that simultaneously performs event association, story linking, cost calculation, and priority determination, allowing a single system to handle multiple tasks that would otherwise require separate processes
Solution Approach 2:
The system automatically associates new events with relevant stories and change requests using machine learning techniques, enabling self-service automation where the system independently processes and prioritizes change requests without manual intervention
Data Source
AI summary
In an approach to linking operational data with issues, a new event is received. The new event is associated to a story, where the story is related to an identified problem within the system, and further where the new event is associated with the story using machine learning techniques. The story is associated to related change requests based on a similarity between the story and related change requests, where the similarity between the story and the related change requests is associated using the machine learning techniques. A cost is calculated for the story. Responsive to associating the new event with a specific change request, the priority of the specific change request is updated based on the cost for the story.